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OJBTM
Online Journal of
Bioinformatics ©
Volume
12(1):1-8, 2011
Support vector
machine classification and prediction of lyases
Lavanya Rishishwar1*, Neha
Mishra1, Bhasker Pant1, Kumud Pant1, Dr. K. R. Pardasani2
Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal,
India
Lavanya Rishishwar
L, Mishra N, Pant B, Pant K, Pardasani KR, Support
Vector Machine r classification and prediction of lyases,
Onl J Bioinform., 12(1):1-9, 2011. A method for functionally
characterizing a novel enzyme by the application of suppo
rt vector machines is
described. Optimal accuracy gained by this self consistency test is 91.42% with
Mathew's Correlation Coefficient (MCC) of 0.57. The method was further
validated by three different types of testing. The resulting accuracy for the
LOO estimate was found to be 90.48% with MCC of 0.59 suggesting that data was
not over fit.
Keywords: Lyases;
Amino Acid Composition; Support Vector Machine; RBF kernel; Polynomial kernel;
GRID.
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